alibayram/feji-first-finetune

FEJI First Fine-Tune

FEJI First Fine-Tune is a Turkish folk music dataset prepared for ACE-Step 1.5 LoRA fine-tuning experiments. It contains 201 audio examples with aligned ACE-Step metadata for caption-conditioned music generation.

The dataset was exported from the local finetune-dataset/ folder and uploaded as Parquet shards with embedded audio. Each row contains one audio sample plus metadata fields used by the ACE-Step training and dataset-builder workflow.

Dataset Details

• Rows: 201
• Default split: train
• Primary language: Turkish (tr)
• Primary domain: Turkish folk / Turku-style music
• ACE-Step custom tag: fejiturkishmakam
• Audio format in source folder: WAV
• Published format: Hugging Face Parquet with embedded audio column

Intended Use

This dataset is intended for:

• ACE-Step 1.5 LoRA / adapter fine-tuning.
• Experiments in Turkish folk music generation.
• Caption-conditioned music generation research using makam, usul, region, instrumentation, lyrics, and tempo metadata.
• Reproducible dataset loading through Hugging Face datasets.

Columns

Important columns include:

Column	Description
audio	Embedded audio sample loaded through datasets.Audio.
id	Stable sample identifier, matching the local audio filename stem.
audio_path	Original local relative audio path from the ACE-Step dataset JSON.
filename	Original audio filename.
caption	Main ACE-Step text-conditioning caption.
lyrics	Structured lyrics used by the lyrics branch.
raw_lyrics	Unformatted lyrics text.
formatted_lyrics	Lyrics with section markers such as [Verse] / [Chorus].
bpm	Estimated or assigned tempo.
keyscale	Key / scale metadata.
timesignature	Time signature metadata.
duration	Duration in seconds.
language	Language code.
is_instrumental	Whether the sample is instrumental.
custom_tag	Trigger tag used for adapter training.
makam	Turkish makam label when available.
usul	Rhythmic/usul label when available.
region	Regional label when available.
instruments	Instrument list.
vocal	Vocal-performance descriptor.
source_url	Source URL recorded in the local manifest.
youtube_id	YouTube ID recorded in the local manifest.
playlist	Source playlist ID recorded in the local manifest.

Loading

from datasets import load_dataset

dataset = load_dataset("alibayram/feji-first-finetune", split="train")
sample = dataset[0]

print(sample["caption"])
print(sample["lyrics"])
print(sample["audio"])

The audio field is decoded by Hugging Face datasets as an audio dictionary containing the waveform array, sampling rate, and original path information.

ACE-Step Usage Notes

For ACE-Step fine-tuning, use the metadata as follows:

• Put instrument, makam, region, style, and performance cues in caption.
• Use lyrics / formatted_lyrics for the lyrics branch.
• Keep the trigger tag fejiturkishmakam consistent across training and generation prompts.
• Use the BPM, key, and time-signature columns when building or validating structured training samples.

The local export script used for this dataset validates that every JSON sample has a matching audio file before upload and preserves every sample metadata field from dataset.json.

Limitations and Rights

This dataset card documents the technical dataset structure and intended model training workflow. Source-rights, redistribution rights, and downstream commercial-use permissions should be verified before public or commercial use.